The personal blog of Adam Nash

Zynga

A couple of days ago, a story broke in the Wall Street Journal about Zynga “leaning” on some early employees to surrender portions of their equity. Not surprisingly, this blew up a bit in the press, leading to a wide number of articles talking about the potential threats to the Silicon Valley equity culture, employment litigation, and a number of fairly serious issues.

As Zynga has indicated that their IPO is imminent, no doubt a lot of this is fueled by the fact that Zynga is a hot company right now. But some of the issues raised are very real, and I thought it might be interesting to lend a different perspective to the story as a opportunity to think more deeply about the challenges leaders face in hyper growth companies, even ones as successful as Zynga.

It takes time to gather data to evaluate an executive’s performance. You can’t evaluate an executive based on her own output, like a normal employee — you have to evaluate her based on the output of her organization. It takes time for her to build and manage her organization to generate output. Therefore, it takes longer to evaluate the performance of an executive than a normal employee.

But, an executive can cause far more damage than a normal employee. A normal employee doesn’t work out, fine, replace him. An executive doesn’t work out, it can — worst case — permanently cripple her function and sometimes the entire company.Therefore, it is far more important to fire a bad executive as fast as possible, versus a normal employee.

Now, the facts of the Zynga story are a bit blurry in the press, but for the purposes of this blog post, I’m assuming the following:

This issue affected a relatively small number of people at Zynga, specifically executive-level hires

These people were identified, over time, as underperformers at the original role they filled

These people still had not vested their equity

Obviously, the above distinctions above matter greatly in terms of the tricky balance of issues around making a decision like this.

It’s worth noting, however, that executives are expensive hires. If an executive is vesting 250K shares per year, and hiring a new engineer or designer costs 10K shares per year, then that person really has to deliver an incredible amount of value to justify their compensation. After all, you could use the money to hire 25 additional engineers. A great leader can easily justify that value (and more) in terms of their power to create long term value for the company, but it’s definitely a high bar to clear.

The Reason for Vesting

Not to be pedantic, but there is a very good reason why employees at tech companies are given equity. Fundamentally, the best corporate cultures in Silicon Valley are based on people working together not to just build technology or products, but actively working to build a great company. Stock ownership is an important part of that culture – when people have meaningful equity in a company, it cements the idea that everyone is a part-owner of the business.

Four years may not seem like a long time, but in truth, hypergrowth tech companies grow and change at rates that seem theoretically impossible. Zynga had 150 employees in 2008. LinkedIn had fewer than 400. As a result, the responsibilities and requirements of almost any position at the company radically change in a year, let alone four years. This is one of the great opportunities that high tech companies afford employees who take advantage of growth to stretch and grow quickly into new responsibilities and experiences. But it’s extremely challenging, and fairly unforgiving as hypergrowth means that every person’s efforts potentially impact dozens of employees going forward and millions of users.

Vesting exists as an important reminder, however, that your share of the company is earned over time, not at signing. You earn your share of the company – every day, every month, every year. For most people, this isn’t an issue, because it is amazing how dedicated people are in Silicon Valley. People are passionate about what they do and the teams they work with, and that passion translates into world-class dedication and effort.

Real Equity, Real Money, Really Tough Decisions

Back to Zynga. Let’s assume, for a second, that you have the situation described in the Wall Street Journal. You’ve identified a small number of relatively high level employees who, for whatever reason, you decide are underperforming their original roles. Normally, there are a couple of options:

Tolerate the under-performance, or compensate for it with additional hires, but let them “vest out” their stock grants despite the fact that they aren’t filling the role that the equity was predicated on.

Fire them.

As per Marc Andreesen’s post, option (1) is toxic. The equity, while material, isn’t the dominant issue. The impact to the company culture can be devastating, and if a repeated pattern, permanently damaging to the ability of the company to attract and retain the best talent and have them do their best work.

Let’s not forget also that we ask our company leaders to be thoughtful of their responsibilities to shareholders as well, particularly in public companies. Executives are expensive hires, and equity allocated to them could always be allocated to hiring other great people. Human beings tend to suffer from “sunk cost fallacy”, and they hate to admit mistakes and take on difficult confrontation. Option (1) swims in all of those issues.

But option (2) doesn’t always feel right in a hyper-growth company either. What if the employee has a number of positive attributes and skills? What if you would gladly hire them today, just in a different role?

From the press, it looks like Zynga tried to find a third way. Rather than fire the employee, offer them the ability to stay at the company in a role that better suits their performance, with compensation to match.

You may not agree with that approach, and I think Semil Shah does a good job in TechCrunch talking about the cultural issues that this type of approach can cause. But it would be foolish not to see that this is really a tough decision, and shouldn’t be trivialized or sensationalized.

Talking vs. Doing

There has never been a shortage of armchair quarterbacks and theorists debating the merits and demerits of different leadership actions and company cultures. It’s part of an ecosystem that rewards thinking and learning.

It’s relatively simple to have a knee-jerk, emotional reaction to a piece like the one in the Wall Street Journal. Let’s face it, that’s part of the reason they published it. Companies like Zynga are amazing, and more importantly, they matter. How they grow, navigate, succeed and fail is part of how we all learn to build better high tech companies.

It’s fairly easy, in fact, to demonize actions that you don’t agree with. However, it’s often a much more productive intellectual path to ask yourself, “Why would good, smart, ethical people do this?” Whether you agree or disagree with the actions taken by Zynga here, these are very hard decisions, and there is a lot for aspiring technology leaders to think about and learn from.

Regular readers of this blog know that I’ve been a huge fan of game mechanics for years. Game mechanics is a loose term for a variety of insights into the neurological and sociological underpinnings of the games that humans like to play. In the past decade, there has been a massive growth in our understanding of game mechanics, even to the point now where you can’t go 10 feet in the Valley without tripping over a venture capitalist dropping the term in conversation.

This past weekend, I had the chance to chat with an old friend from a former start-up, and I was talking about why I love Zynga, and why game mechanics were one of the more interesting product insights to come out the last few years of product design. The conversation moved on to catching up on old friends and careers, and the obvious hit me: our very careers in Silicon Valley are based on game mechanics.

I’m going to grotesquely simplify the concept for the purposes of this post. Real students of psychology & neurobiology – hold your nose while you go through this section.

It turns out that there are demonstrated patterns for response (neé addiction) for different types of reward systems:

Simple: You hit the lever, you get a treat. Most animals will understand and play this game. (Hello, Pavlov)

Variable Interval: You hit the lever, but sometimes you get a treat, sometimes not. This game turns out to be even more addictive, likely due to the combination of uncertainty (triggers fight-or-flight) and then the rush of the intermittent reward when it comes. (When you go to puppy school, you learn to *not* give your dog a treat every single time they do something right.)

Variable Interval, Variable Payout. The most addictive of games. You hit the lever, and sometimes you get a treat, and sometimes you don’t. But sometimes the treat is big, and sometimes the treat is small. (Hello, slot machine)

I was explaining this fact to my friend, when it occurred to me that this is the game that we all play in Silicon Valley.

Addiction: Hypergrowth Tech Companies

This pattern explains a lot about why Silicon Valley is so… addicting. Venture capitalists invest capital into startups seeking outstanding returns. Most engineers, on the other hand, invest their human capital to get the same result. Engineers join hypergrowth companies with the assumption of receiving an equity stake. That equity stake is the difference between making a good salary, and potentially hitting a step-function in their net worth.

Let’s play out the reward pattern:

Variable Interval: Tenure at tech companies can be anywhere from a few months to a few decades, however it averages about 2-3 years. Sometimes startups go bankrupt less than 2 years after you join or found them. Sometimes they get acquired. Sometimes they become truly large, successful ongoing companies. The timing definitely varies. Many people would count themselves lucky if one in three of the companies they join turns out to be successful at a level that provides a meaningful value for their equity.

There are many aspects to economics behind video games that have been largely unchanged over the past two decades. Fundamentally, Zynga lept to an opportunity to take advantage of a social platform (Facebook) to challenge some of the fundamental limitations of distribution and monetization that plagued the software giants who dominated desktop and platform gaming.If you need a new gaming mouse check out the best gaming mouse for small hands that might fit you perfectly.

Obviously, I am a fan of the company. The number of blog posts here about Zynga games should tell you that. But when people ask me in real life why I’m such a big fan of Zynga, I give them a simple tongue-in-cheek thesis.

Selling Things You Don’t Need

It’s a well know fact that selling people things they don’t need is a great business. Some might say it’s when retailers and/or products rise higher in the Maslow hierarchy of needs. By definition, when items rise up that motivation chain, more powerful emotions come into play. Fundamentally, no one needs a cotton candy tree. But Zynga gets to the emotions of why you might want one.

In the end, the willingness to pay for things you don’t need is shockingly high in an economy where people have disposable income.

Selling Things You Don’t Need that Don’t Exist

Hundreds of years ago, this was what selling “snake oil” was all about. Selling something that you don’t need, and that doesn’t exist has always been a great way to make money. Unfortunately, it also used to be a sure fire path to getting run out of town (and perhaps tarred & feathered in the process).

A little computer icon of a purple cow does not exist, and you don’t need it. But that doesn’t change the fact that Zynga has found a way not only to make you want it, but deliver it to you with an effective cost of goods sold of approximately zero.

So now we have a high willingness to pay, combined with low friction and low cost of goods sold.

Selling Things You Don’t Need, That Don’t Exist, and That Are Addictive

This might be called the holy trinity of virtual goods, but in the end, this is the most amazing part of the Zynga model. Certain types of social interaction are clearly pleasurable to people at a fundamental level. We love the inherent stimulation in getting a response, recognition or even just insight into another human being. Once we find a path for these interactions, we want more of it. By leveraging a social platform for its games, Zynga has integrated social stimulation into their economics with outstanding results.

So now we have a high willingness to pay, combined with low friction and low cost of goods sold, with relatively low distribution costs and a high propensity for repeat activity.

Any wonder that I wish I owned Zynga stock?

Congratulations (in advance) to all of my great friends on the Zynga team.

The “Spice Rack” is a concept I have advocated previously for Farmville. A mechanism to purchase items that would accelerate / change the equations for existing actions. (My original request was for increased levels in Farmville to actually accelerate the length of time it would take you to harvest any crop, like a 10% cut in time, etc.)

For this analysis, I’ve started with the simplest spices: Super Salt and Power Pepper.

For each dish, I calculated the increase (or decrease) in profit for buying the spice and applying it to one dish for the cycle. I assume that Café World rounds down when you apply the 5% or 10% increase in number of servings. I express the number as an “Return on Investment” percentage (ROI) on the cost of the spice.

So, for example, if spending 600 coins on Power Pepper yield an extra 150 coins of profit after subtracting the cost of the pepper, I describe that as a “25% ROI” for Pepper for that dish.

Results of Spiceonomics

There are a few very interesting takeaways from the table below:

Spices are rarely worth it. Salt & Pepper have negative ROIs for almost all dishes. In fact, in the history of the game, only 9 dishes are profitable when using the spices. Interestingly, Grand Tandoori Chicken is net neutral (ROI = 0%).

Spices help more advanced players. Almost all the dishes with positive ROI are at the higher levels.

Spices help infrequent players more. The way the numbers work out, all the dishes where spices help are longer cooking time dishes. This is good for players that might only play the game once a day (say, in the evening).

Have trouble figuring out whether Mystical Pizza is a good dish? Deciding on whether to make the Dino Egg or Rackasaurus Ribs? My Google Doc is now updated with tables for all 62 Cafe World dishes for data, and color coded based the cooking time of each dish, to help make picking the right dish easy. Rather than cut & paste everything here, I’m going to just link to the doc.

So I take the time to create a whole new post for Café World in 2010, and what does Zynga do? They roll out some new crazy dishes based on an alien invasion, and now I’m 1.6M Café coins poorer. Oh well.

I find it fairly interesting that Zynga is clearly mapping the same thematic variants from Farmville to their other games. I remember when they did the space theme for Farmville (I still have 5 alien cows that produce Milktonium as proof…)

I won’t repeat the previous analysis. As a reminder, all of these numbers assume:

The numbers are per dish, per stove

The numbers assume the cost (15 coins) and experience (+1) of cleaning the stove each cycle

Profit & Cafe Points tables assume “instant” cleaning time.

Real World Hourly Wages assumes a cleaning time of 1 minute per stove.

You can read my previous posts for the rational behind these assumptions.

Profit per Dish

Here are the dishes, sorted by profitability as measured by profit per dish per day.

Dish

Profit / Cycle

Cycle Time

Profit / Day

V.I.P. Dinner

9,786.00

1,080.00

13,048.00

Bacon Cheeseburger

22.00

5.00

6,336.00

Overstuffed Peppers

2,985.00

720.00

5,970.00

Kung Pao Stir Fry

985.00

240.00

5,910.00

Delicious Chocolate Cake

3,435.00

840.00

5,888.57

Fiery Fish Tacos

490.00

120.00

5,880.00

Lemon Butter Lobster

485.00

120.00

5,820.00

Martian Brain Bake

5,585.00

1,440.00

5,585.00

Shu Mai Dumplings

1,355.00

360.00

5,420.00

King Crab Bisque

5,370.00

1,440.00

5,370.00

Lavish Lamb Curry

1,785.00

480.00

5,355.00

Chips and Guacamole

11.00

3.00

5,280.00

Impossible Quiche

10,185.00

2,880.00

5,092.50

Powdered French Toast

67.00

20.00

4,824.00

Super Chunk Fruit Salad

50.00

15.00

4,800.00

Atomic Buffalo Wings

595.00

180.00

4,760.00

Jammin’ Jelly Donuts

65.00

20.00

4,680.00

Smoked Salmon Latkes

385.00

120.00

4,620.00

Tostada de Carne Asada

1,485.00

480.00

4,455.00

Buttermilk Pancakes

135.00

45.00

4,320.00

Tony’s Classic Pizza

885.00

300.00

4,248.00

Stardust Stew

1,535.00

540.00

4,093.33

Chicken Gyro and Fries

28.00

10.00

4,032.00

Grand Tandoori Chicken

3,985.00

1,440.00

3,985.00

Voodoo Chicken Salad

1,960.00

720.00

3,920.00

Chicken Pot Pie

7,585.00

2,880.00

3,792.50

Herbed Halibut

3,785.00

1,440.00

3,785.00

Sweet Seasonal Ham

1,885.00

720.00

3,770.00

Crackling Peking Duck

2,685.00

1,080.00

3,580.00

Jumbo Shrimp Cocktail

68.00

30.00

3,264.00

Savory Stuffed Turkey

2,885.00

1,320.00

3,147.27

Tikka Masala Kabobs

130.00

60.00

3,120.00

Macaroni and Cheese

245.00

120.00

2,940.00

Crème Fraiche Caviar

57.00

30.00

2,736.00

Spaghetti and Meatballs

910.00

480.00

2,730.00

Gingerbread House

13,485.00

7,200.00

2,697.00

Spitfire Roasted Chicken

2,585.00

1,440.00

2,585.00

French Onion Soup

425.00

240.00

2,550.00

Triple Berry Cheesecake

1,235.00

720.00

2,470.00

Caramel Apples

195.00

120.00

2,340.00

Homestyle Pot Roast

3,935.00

2,880.00

1,967.50

Vampire Staked Steak

1,695.00

1,440.00

1,695.00

Pumpkin Pie

845.00

720.00

1,690.00

Café Points per Dish

Here are the dishes, sorted by café points per dish per day.

Dish

Café Points / Cycle

Cycle Time

Café Points / Day

Bacon Cheeseburger

7.00

5.00

2,016.00

Chicken Gyro and Fries

14.00

10.00

2,016.00

Chips and Guacamole

4.00

3.00

1,920.00

Powdered French Toast

21.00

20.00

1,512.00

Super Chunk Fruit Salad

14.00

15.00

1,344.00

Jammin’ Jelly Donuts

15.00

20.00

1,080.00

Crème Fraiche Caviar

22.00

30.00

1,056.00

Jumbo Shrimp Cocktail

21.00

30.00

1,008.00

Buttermilk Pancakes

31.00

45.00

992.00

Lemon Butter Lobster

68.00

120.00

816.00

Smoked Salmon Latkes

63.00

120.00

756.00

Shu Mai Dumplings

156.00

360.00

624.00

Lavish Lamb Curry

200.00

480.00

600.00

Fiery Fish Tacos

49.00

120.00

588.00

Atomic Buffalo Wings

68.00

180.00

544.00

Tikka Masala Kabobs

22.00

60.00

528.00

Macaroni and Cheese

41.00

120.00

492.00

Delicious Chocolate Cake

273.00

840.00

468.00

Kung Pao Stir Fry

75.00

240.00

450.00

Savory Stuffed Turkey

403.00

1,320.00

439.64

Caramel Apples

35.00

120.00

420.00

Overstuffed Peppers

206.00

720.00

412.00

Grand Tandoori Chicken

403.00

1,440.00

403.00

Stardust Stew

139.00

540.00

370.67

Tostada de Carne Asada

123.00

480.00

369.00

French Onion Soup

61.00

240.00

366.00

Voodoo Chicken Salad

168.00

720.00

336.00

Tony’s Classic Pizza

68.00

300.00

326.40

Martian Brain Bake

314.00

1,440.00

314.00

Spaghetti and Meatballs

100.00

480.00

300.00

Triple Berry Cheesecake

140.00

720.00

280.00

King Crab Bisque

252.00

1,440.00

252.00

V.I.P. Dinner

175.00

1,080.00

233.33

Herbed Halibut

225.00

1,440.00

225.00

Crackling Peking Duck

166.00

1,080.00

221.33

Gingerbread House

1,063.00

7,200.00

212.60

Spitfire Roasted Chicken

210.00

1,440.00

210.00

Sweet Seasonal Ham

102.00

720.00

204.00

Impossible Quiche

351.00

2,880.00

175.50

Chicken Pot Pie

307.00

2,880.00

153.50

Pumpkin Pie

76.00

720.00

152.00

Homestyle Pot Roast

279.00

2,880.00

139.50

Vampire Staked Steak

113.00

1,440.00

113.00

Real World Hourly Wage per Dish

Here are the dishes, sorted by the real world hourly wage for each dish per day, in US dollars.

Dish

$ / Hour (Low)

$ / Hour (High)

Gingerbread House

121.35

264.23

Impossible Quiche

91.66

199.57

V.I.P. Dinner

88.07

191.75

Chicken Pot Pie

68.26

148.62

Martian Brain Bake

50.26

109.43

King Crab Bisque

48.33

105.22

Grand Tandoori Chicken

35.86

78.08

Homestyle Pot Roast

35.41

77.10

Herbed Halibut

34.06

74.16

Delicious Chocolate Cake

30.91

67.31

Overstuffed Peppers

26.86

58.49

Savory Stuffed Turkey

25.96

56.53

Crackling Peking Duck

24.16

52.61

Spitfire Roasted Chicken

23.26

50.65

Voodoo Chicken Salad

17.64

38.40

Sweet Seasonal Ham

16.96

36.94

Lavish Lamb Curry

16.06

34.98

Vampire Staked Steak

15.25

33.21

Stardust Stew

13.81

30.08

Tostada de Carne Asada

13.36

29.10

Shu Mai Dumplings

12.19

26.55

Triple Berry Cheesecake

11.11

24.20

Kung Pao Stir Fry

8.86

19.30

Spaghetti and Meatballs

8.19

17.83

Tony’s Classic Pizza

7.96

17.34

Pumpkin Pie

7.60

16.56

Atomic Buffalo Wings

5.35

11.66

Fiery Fish Tacos

4.41

9.60

Lemon Butter Lobster

4.36

9.50

French Onion Soup

3.82

8.33

Smoked Salmon Latkes

3.46

7.54

Macaroni and Cheese

2.20

4.80

Caramel Apples

1.75

3.82

Buttermilk Pancakes

1.21

2.65

Tikka Masala Kabobs

1.17

2.55

Jumbo Shrimp Cocktail

0.61

1.33

Powdered French Toast

0.60

1.31

Jammin’ Jelly Donuts

0.58

1.27

Crème Fraiche Caviar

0.51

1.12

Super Chunk Fruit Salad

0.45

0.98

Chicken Gyro and Fries

0.25

0.55

Bacon Cheeseburger

0.20

0.43

Chips and Guacamole

0.10

0.22

Special Bonus: I’ve now moved my spreadsheet over to this Google Spreadsheet. Now you can see all the rows of calculation for some insight into Café World Economics. As usual, let me know if you find mistakes or have questions…

Updates:

I’ve added the following posts on Café World Economics since this one.

What better way to spend the waning hours of the first day of the new decade than to update all of the tables for the new dishes on Café World? Zynga has added a number of new dishes over the past few weeks, so it’s about time for updated data on all the dishes.

I won’t repeat the previous analysis. As a reminder, all of these numbers assume:

The numbers are per dish, per stove

The numbers assume the cost (15 coins) and experience (+1) of cleaning the stove each cycle

Profit & Cafe Points tables assume “instant” cleaning time.

Real World Hourly Wages assumes a cleaning time of 1 minute per stove.

You can read my previous posts for the rational behind these assumptions.

How to use these tables. For me, I use the tables as follows: If I know I won’t be able to check on my Café for the next 24 hours, I go down the table I’m trying to optimize for (money or experience) and I look for the first dish in the list that is 1440 minutes AND that I have enough experience to cook. For example, I’m currently at level 42, so if I’m looking for a “1 day” dish, the first one for experience is Grand Tandoori Chicken. But since I can’t buy that yet, I have to keep going down until I hit King Crab Bisque.

Table #1: Profit per dish

Dish

Profit / Day

Profit / Hour

Min Per Cycle

Bacon Cheeseburger

6336.0

264.0

5.0

Overstuffed Peppers

5970.0

248.8

720.0

Kung Pao Stir Fry

5910.0

246.3

240.0

Delicious Chocolate Cake

5888.6

245.4

840.0

Fiery Fish Tacos

5880.0

245.0

120.0

Lemon Butter Lobster

5820.0

242.5

120.0

Shu Mai Dumplings

5420.0

225.8

360.0

King Crab Bisque

5370.0

223.8

1440.0

Lavish Lamb Curry

5355.0

223.1

480.0

Chips and Guacamole

5280.0

220.0

3.0

Impossible Quiche

5092.5

212.2

2880.0

Powdered French Toast

4824.0

201.0

20.0

Super Chunk Fruit Salad

4800.0

200.0

15.0

Atomic Buffalo Wings

4760.0

198.3

180.0

Jammin’ Jelly Donuts

4680.0

195.0

20.0

Smoked Salmon Latkes

4620.0

192.5

120.0

Tostada de Carne Asada

4455.0

185.6

480.0

Buttermilk Pancakes

4320.0

180.0

45.0

Tony’s Classic Pizza

4248.0

177.0

300.0

Chicken Gyro and Fries

4032.0

168.0

10.0

Grand Tandoori Chicken

3985.0

166.0

1440.0

Voodoo Chicken Salad

3920.0

163.3

720.0

Chicken Pot Pie

3792.5

158.0

2880.0

Herbed Halibut

3785.0

157.7

1440.0

Sweet Seasonal Ham

3770.0

157.1

720.0

Crackling Peking Duck

3580.0

149.2

1080.0

Jumbo Shrimp Cocktail

3264.0

136.0

30.0

Savory Stuffed Turkey

3147.3

131.1

1320.0

Tikka Masala Kabobs

3120.0

130.0

60.0

Macaroni and Cheese

2940.0

122.5

120.0

Crème Fraiche Caviar

2736.0

114.0

30.0

Spaghetti and Meatballs

2730.0

113.8

480.0

Gingerbread House

2697.0

112.4

7200.0

Spitfire Roasted Chicken

2585.0

107.7

1440.0

French Onion Soup

2550.0

106.3

240.0

Triple Berry Cheesecake

2470.0

102.9

720.0

Caramel Apples

2340.0

97.5

120.0

Homestyle Pot Roast

1967.5

82.0

2880.0

Vampire Staked Steak

1695.0

70.6

1440.0

Pumpkin Pie

1690.0

70.4

720.0

Table #2: Café Points per dish

Dish

CP / Day

CP / Hour

Min Per Cycle

Chicken Gyro and Fries

2016.0

84.0

10.0

Bacon Cheeseburger

2016.0

84.0

5.0

Chips and Guacamole

1920.0

80.0

3.0

Powdered French Toast

1512.0

63.0

20.0

Super Chunk Fruit Salad

1344.0

56.0

15.0

Jammin’ Jelly Donuts

1080.0

45.0

20.0

Crème Fraiche Caviar

1056.0

44.0

30.0

Jumbo Shrimp Cocktail

1008.0

42.0

30.0

Buttermilk Pancakes

992.0

41.3

45.0

Lemon Butter Lobster

816.0

34.0

120.0

Smoked Salmon Latkes

756.0

31.5

120.0

Shu Mai Dumplings

624.0

26.0

360.0

Lavish Lamb Curry

600.0

25.0

480.0

Fiery Fish Tacos

588.0

24.5

120.0

Atomic Buffalo Wings

544.0

22.7

180.0

Tikka Masala Kabobs

528.0

22.0

60.0

Macaroni and Cheese

492.0

20.5

120.0

Delicious Chocolate Cake

468.0

19.5

840.0

Kung Pao Stir Fry

450.0

18.8

240.0

Caramel Apples

420.0

17.5

120.0

Overstuffed Peppers

412.0

17.2

720.0

Grand Tandoori Chicken

403.0

16.8

1440.0

Tostada de Carne Asada

369.0

15.4

480.0

French Onion Soup

366.0

15.3

240.0

Voodoo Chicken Salad

336.0

14.0

720.0

Tony’s Classic Pizza

326.4

13.6

300.0

Spaghetti and Meatballs

300.0

12.5

480.0

Triple Berry Cheesecake

280.0

11.7

720.0

King Crab Bisque

252.0

10.5

1440.0

Savory Stuffed Turkey

235.6

9.8

1320.0

Herbed Halibut

225.0

9.4

1440.0

Crackling Peking Duck

221.3

9.2

1080.0

Gingerbread House

212.6

8.9

7200.0

Spitfire Roasted Chicken

210.0

8.8

1440.0

Sweet Seasonal Ham

204.0

8.5

720.0

Impossible Quiche

175.5

7.3

2880.0

Chicken Pot Pie

153.5

6.4

2880.0

Pumpkin Pie

152.0

6.3

720.0

Homestyle Pot Roast

139.5

5.8

2880.0

Vampire Staked Steak

113.0

4.7

1440.0

Table #3: Real World Hourly Wages per dish

Dish

Hourly Wage (high)

Hourly Wage (low)

Gingerbread House

$264.23

$121.36

Impossible Quiche

$199.57

$91.66

Chicken Pot Pie

$148.62

$68.26

King Crab Bisque

$105.22

$48.33

Grand Tandoori Chicken

$78.08

$35.86

Homestyle Pot Roast

$77.10

$35.41

Herbed Halibut

$74.16

$34.06

Delicious Chocolate Cake

$67.31

$30.91

Overstuffed Peppers

$58.49

$26.86

Savory Stuffed Turkey

$56.53

$25.96

Crackling Peking Duck

$52.61

$24.16

Spitfire Roasted Chicken

$50.65

$23.26

Voodoo Chicken Salad

$38.40

$17.64

Sweet Seasonal Ham

$36.94

$16.96

Lavish Lamb Curry

$34.98

$16.06

Vampire Staked Steak

$33.21

$15.25

Tostada de Carne Asada

$29.10

$13.36

Shu Mai Dumplings

$26.55

$12.19

Triple Berry Cheesecake

$24.20

$11.11

Kung Pao Stir Fry

$19.30

$8.86

Spaghetti and Meatballs

$17.83

$8.19

Tony’s Classic Pizza

$17.34

$7.96

Pumpkin Pie

$16.56

$7.60

Atomic Buffalo Wings

$11.66

$5.35

Fiery Fish Tacos

$9.60

$4.41

Lemon Butter Lobster

$9.50

$4.36

French Onion Soup

$8.33

$3.82

Smoked Salmon Latkes

$7.54

$3.46

Macaroni and Cheese

$4.80

$2.20

Caramel Apples

$3.82

$1.75

Buttermilk Pancakes

$2.65

$1.21

Tikka Masala Kabobs

$2.55

$1.17

Jumbo Shrimp Cocktail

$1.33

$0.61

Powdered French Toast

$1.31

$0.60

Jammin’ Jelly Donuts

$1.27

$0.58

Crème Fraiche Caviar

$1.12

$0.51

Super Chunk Fruit Salad

$0.98

$0.45

Chicken Gyro and Fries

$0.55

$0.25

Bacon Cheeseburger

$0.43

$0.20

Chips and Guacamole

$0.22

$0.10

Once again, a thank you to Simple Think, which continues to have the most up-to-date raw data on Café World dishes at all levels…

The traffic to my blog from my first two Fishville blog posts has been staggering. How can I resist? That’s right, it’s time for Yet Another Fishville Post (YAFP). Come on, you know you want to read more…

I’ve been a little surprised to see how few accurate blog posts exist out on the web that break down the profit & experience for Fishville. For reference you can still find my first two blogs posts here:

I’m at Level 42 in Fishville, so I can get almost all of the data myself. However, I’m still missing the data for the last two fish:

Blueline Trigger

Longhorn Clownfish

If you have the data on either of these two fish, please post here in the comments.

In the past few weeks, Zynga has rolled out a number of new fish. I’ve updated my Google Doc with all the updated numbers.

The most interesting addition has been a series of fish that you can only purchase with Sand Dollars, which is the Fishville denomination for game money that you have to buy with real money.

This poses a dilemma for my calculations, since I base profitability on coins spent to coins earned. As a result, I needed a conversion ratio from Sand Dollars to Coins. Although you can’t buy Sand Dollars with Coins, you can buy both with real US dollars ($) from Zynga with a scaling price table:

Dollars

Coins

Sand Dollars

Coins / $

SD / $

Coins / SD

5

7500

25

1500

5

300.00

10

15800

55

1580

5.5

287.27

20

33300

115

1665

5.75

289.57

40

70600

240

1765

6

294.17

Notice anything strange?

According to this table, the ratio of coins to sand dollars varies between 300 and 287, and in a non-linear fashion. It’s as if Zynga didn’t compare the volume discount on coins to the volume discount to sand dollars when they generated these prices.

Since it’s non-linear, I decided to take the “average” ratio as my conversion. So, for the purposes of this blog post, one sand dollar = 292.75 coins.

Using that ratio, I was able to regenerate my graphs. Here is the graph showing profitability of each fish, per level. All the assumptions from my second blog post still hold:

What you’ll notice is that some of the “sand dollar” fish are actually money losers for the first two levels. That’s right, assuming my conversion ratio, you’d be better off just buying coins with your money, rather than buying sand dollars and then growing these fish!

Now, the updated experience points chart tells a different tale:

In this case, you can clearly see that the best fish for experience, excluding the “fast fish”, are the sand dollar fish. As a result, it’s pretty clear that what you are buying with your sand dollars is a fast path to rise up levels. If you’re willing to spend the money on Batfish, you’ll be able to climb those levels quickly, and with much less work than minding 5 minute fish…

You can reference the full data in my Google Doc. Let me know if you see any issues with the calculations.

For reference, I’ll include the Level 1 tables here, just in case there are issues reading the now huge Google Doc.